1. Field of the Invention
The present invention is directed to a neural node network with local feedback and crosstalk along with a method of teaching same and, more particularly, to a feed forward neural network with local node feedback and crosstalk between nodes within a layer, in which the learning method includes feed forward weight modification along with propagation of the local feedback and crosstalk during the weight modification, with the network being used to develop a model of an actual system, where the model can be used to determine hidden states or parameters of the actual system or expected outputs of the actual system to possible input changes.
2. Description of the Related Art
Conventional perception based neural networks include input and output layers and hidden layers between the input and output layers. The hidden layers are fully connected, that is each node in a hidden layer is connected to all the nodes in a prior layer and to all nodes in a subsequent layer. Each of the nodes conventionally sums weighted inputs to the node and then performs a transfer function operation such as a threshold comparison operation to produce an output to the next layer. The transfer function is sometimes called a transform function, an activation function, a gain function, a squashing function, a threshold function, or a sigmoid function. Since the introduction of recurrent neural networks by Hopfield a number of researchers have considered various architectures and learning algorithms. The most prominent among these are (1) the real time recurrent learning which uses a purely feedback network, (2) back propagation through time which uses a purely feed forward network, (3) recurrent back propagation trained to recognize fixed points, (4) the use of the previous approach to learning the trajectory of unforced systems, (5) dynamic back propagation and (6) the grouping of feedback links as nodes of a feed forward network. None of these architectures provides a suitable network for modeling and controlling dynamic nonlinear systems and none of the learning methods are particularly efficient at converging to an optimum solution for dynamic nonlinear systems.